Factors Influencing Anemia and Iron Deficiency in Pregnant Women in Indonesia

Pregnant women are at high risk for anemia, iron deficiency (ID), and iron deficiency anemia (IDA). During pregnancy, it is associated with low birth weight, preterm delivery, and an increased risk of perinatal death. The condition of anemia, ID, IDA, can be predicted using the regression method multinomial logistics. However, the classification method is prone to unbalanced data. One method that can be used to overcome the problem of data imbalance is Random Oversampling (ROS). The results of the multinomial logistic regression method show that the factors that significantly influence anemia and iron deficiency are gestational age, nutritional status, history of malaria, education, occupation, fruit consumption, and vegetable consumption. The multinomial logistic regression model provides a classification accuracy of 45.556%.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-13
DOI
https://doi.org/10.5281/zenodo.15205147
Primary Topic
Statistical Methods in Epidemiology
Type
article
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article

Factors Influencing Anemia and Iron Deficiency in Pregnant Women in Indonesia

Zenodo (CERN European Organization for Nuclear Research)
Statistical Methods in Epidemiology
article

Factors Influencing Anemia and Iron Deficiency in Pregnant Women in Indonesia

article en

Abstract

Pregnant women are at high risk for anemia, iron deficiency (ID), and iron deficiency anemia (IDA). During pregnancy, it is associated with low birth weight, preterm delivery, and an increased risk of perinatal death. The condition of anemia, ID, IDA, can be predicted using the regression method multinomial logistics. However, the classification method is prone to unbalanced data. One method that can be used to overcome the problem of data imbalance is Random Oversampling (ROS). The results of the multinomial logistic regression method show that the factors that significantly influence anemia and iron deficiency are gestational age, nutritional status, history of malaria, education, occupation, fruit consumption, and vegetable consumption. The multinomial logistic regression model provides a classification accuracy of 45.556%.

Zenodo (CERN European Organization for Nuclear Research)
Universitas Airlangga (ID), IPB University (ID), Public Health Agency (GB)
Zero hunger
Openalex Percentile: Top 47%
Statistical Methods in Epidemiology
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